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Archive for the ‘Population Health Management, Genetics & Pharmaceutical’ Category

Sperm damage and fertility problem due to COVID-19

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Many couples initially deferred attempts at pregnancy or delayed fertility care due to concerns about coronavirus disease 2019 (COVID-19). One significant fear during the COVID-19 pandemic was the possibility of sexual transmission. Many couples have since resumed fertility care while accepting the various uncertainties associated with severe acute respiratory syndrome coronavirus 2 (SARS-Cov2), including the evolving knowledge related to male reproductive health. Significant research has been conducted exploring viral shedding, tropism, sexual transmission, the impact of male reproductive hormones, and possible implications to semen quality. However, to date, limited definitive evidence exists regarding many of these aspects, creating a challenging landscape for both patients and physicians to obtain and provide the best clinical care.

According to a new study, which looked at sperm quality in patients who suffered symptomatic coronavirus (COVID-19) infections, showed that it could impact fertility for weeks after recovery from the virus. The data showed 60% COVID-19 infected men had reduction in sperm motility and 37% had drop in sperm count, but, 2 months after recovery from COVID-19 the value came down to 28% and 6% respectively. The researchers also of the view that COVID-19 could not be sexually transmitted through semen after a person had recovered from illness. Patients with mild and severe cases of COVID-19 showed similar rate of drop in sperm quality. But further work is required to establish whether or not COVID-19 could have a longer-term impact on fertility. The estimated recovery time is three months, but further follow-up studies are still required to confirm this and to determine if permanent damage occurred in a minority of men.

Some viruses like influenza are already known to damage sperm mainly by increasing body temperature. But in the case of COVID-19, the researchers found no link between the presence or severity of fever and sperm quality. Tests showed that higher concentrations of specific COVID-19 antibodies in patients’ blood serum were strongly correlated with reduced sperm function. So, it was believed the sperm quality reduction cause could be linked to the body’s immune response to the virus. While the study showed that there was no COVID-19 RNA present in the semen of patients who had got over the virus, the fact that antibodies were attacking sperm suggests the virus may cross the blood-testis barrier during the peak of an infection.

It was found in a previous report that SARS-CoV-2 can be present in the semen of patients with COVID-19, and SARS-CoV-2 may still be detected in the semen of recovering patients. Due to imperfect blood-testes/deferens/epididymis barriers, SARS-CoV-2 might be seeded to the male reproductive tract, especially in the presence of systemic local inflammation. Even if the virus cannot replicate in the male reproductive system, it may persist, possibly resulting from the privileged immunity of testes.

If it could be proved that SARS-CoV-2 can be transmitted sexually in future studies, sexual transmission might be a critical part of the prevention of transmission, especially considering the fact that SARS-CoV-2 was detected in the semen of recovering patients. Abstinence or condom use might be considered as preventive means for these patients. In addition, it is worth noting that there is a need for studies monitoring fetal development. Therefore, to avoid contact with the patient’s saliva and blood may not be enough, since the survival of SARS-CoV-2 in a recovering patient’s semen maintains the likelihood to infect others. But further studies are required with respect to the detailed information about virus shedding, survival time, and concentration in semen.

References:

https://www.euronews.com/next/2021/12/21/covid-can-damage-sperm-for-months-making-it-harder-to-conceive-a-baby-a-new-study-finds

https://www.fertstert.org/article/S0015-0282(20)32780-1/fulltext

https://www.fertstertreviews.org/article/S2666-5719(21)00004-9/fulltext

https://www.fertstertscience.org/article/S2666-335X(21)00064-1/fulltext

https://www.fertstert.org/article/S0015-0282(21)02156-7/fulltext

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2765654/

https://www.fertstert.org/article/S0015-0282(21)01398-4/fulltext

https://www.euronews.com/next/2021/08/27/do-covid-vaccines-affect-pregnancy-fertility-or-periods-we-asked-the-world-health-organiza

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Infertility has been primarily treated as a female predicament but around one-half of infertility cases can be tracked to male factors. Clinically, male infertility is typically determined using measures of semen quality recommended by World Health Organization (WHO). A major limitation, however, is that standard semen analyses are relatively poor predictors of reproductive capacity and success. Despite major advances in understanding the molecular and cellular functions in sperm over the last several decades, semen analyses remain the primary method to assess male fecundity and fertility.

Chronological age is a significant determinant of human fecundity and fertility. The disease burden of infertility is likely to continue to rise as parental age at the time of conception has been steadily increasing. While the emphasis has been on the effects of advanced maternal age on adverse reproductive and offspring health, new evidence suggests that, irrespective of maternal age, higher male age contributes to longer time-to-conception, poor pregnancy outcomes and adverse health of the offspring in later life. The effect of chronological age on the genomic landscape of DNA methylation is profound and likely occurs through the accumulation of maintenance errors of DNA methylation over the lifespan, which have been originally described as epigenetic drift.

In recent years, the strong relation between age and DNA methylation profiles has enabled the development of statistical models to estimate biological age in most somatic tissue via different epigenetic ‘clock’ metrics, such as DNA methylation age and epigenetic age acceleration, which describe the degree to which predicted biological age deviates from chronological age. In turn, these epigenetic clock metrics have emerged as novel biomarkers of a host of phenotypes such as allergy and asthma in children, early menopause, increased incidence of cancer types and cardiovascular-related diseases, frailty and cognitive decline in adults. They also display good predictive ability for cancer, cardiovascular and all-cause mortality.

Epigenetic clock metrics are powerful tools to better understand the aging process in somatic tissue as well as their associations with adverse disease outcomes and mortality. Only a few studies have constructed epigenetic clocks specific to male germ cells and only one study reported that smokers trended toward an increased epigenetic age compared to non-smokers. These results indicate that sperm epigenetic clocks hold promise as a novel biomarker for reproductive health and/or environmental exposures. However, the relation between sperm epigenetic clocks and reproductive outcomes has not been examined.

There is a critical need for new measures of male fecundity for assessing overall reproductive success among couples in the general population. Data shows that sperm epigenetic clocks may fulfill this need as a novel biomarker that predicts pregnancy success among couples not seeking fertility treatment. Such a summary measure of sperm biological age is of clinical importance as it allows couples in the general population to realize their probability of achieving pregnancy during natural intercourse, thereby informing and expediting potential infertility treatment decisions. With the ability to customize high throughput DNA methylation arrays and capture sequencing approaches, the integration of the epigenetic clocks as part of standard clinical care can enhance our understanding of idiopathic infertility and the paternal contribution to reproductive success and offspring health.

References:

https://academic.oup.com/humrep/advance-article/doi/10.1093/humrep/deac084/6583111?login=false

https://pubmed.ncbi.nlm.nih.gov/33317634/

https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-019-0656-7

https://pubmed.ncbi.nlm.nih.gov/19319879/

https://pubmed.ncbi.nlm.nih.gov/31901222/

https://pubmed.ncbi.nlm.nih.gov/25928123/

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The durability of T cells versus the triggered of high levels of antibodies: Rationale for the development of T cells focused vaccines

Reporters and Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

Scientists to FDA: Don’t forget about T cells

In the face of waning antibody immunity to the coronavirus, scientists demand more attention on T cell immunity which may be more durable

 

A group of nearly 70 academic scientists, doctors, and biotech leaders sent a letter with an unusual request to the US Food and Drug Administration on Thursday: Please pay more attention to T cells, an overlooked part of the immune system that helps clear up viral infections.

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The Framingham Study: Across 6 Decades, Cardiovascular Disease Among Middle-Aged Adults – mean life expectancy increased and the RLR of ASCVD decreased. Effective primary prevention efforts and better screening increased.

Reporter: Aviva Lev-Ari, PhD, RN

Temporal Trends in the Remaining Lifetime Risk of Cardiovascular Disease Among Middle-Aged Adults Across 6 Decades: The Framingham Study

Ramachandran S. Vasan

Danielle M Enserro

Vanessa Xanthakis

Alexa S Beiser

 and 

Sudha Seshadri

Originally published 18 Apr 2022

https://doi.org/10.1161/CIRCULATIONAHA.121.057889 Circulation. 2022;0

Background: The remaining lifetime risk (RLR) is the probability of developing an outcome over the remainder of one’s lifespan at any given age. The RLR for atherosclerotic cardiovascular disease (ASCVD) in three 20-year periods were assessed using data from a single community-based cohort study of predominantly White participants

Methods: Longitudinal data from the Framingham study in 3 epochs (epoch 1, 1960-1979; epoch 2, 1980-1999; epoch 3, 2000-2018) were evaluated. The RLR of a first ASCVD event (myocardial infarction, coronary heart disease death, or stroke) from 45 years of age (adjusting for competing risk of death) in the 3 epochs were compared overall, and according to the following strata: sex, body mass index, blood pressure and cholesterol categories, diabetes, smoking, and Framingham risk score groups.

Results: There were 317 849 person-years of observations during the 3 epochs (56% women; 94% White) and 4855 deaths occurred. Life expectancy rose by 10.1 years (men) to 11.9 years (women) across the 3 epochs. There were 1085 ASCVD events over the course of 91 330 person-years in epoch 1, 1330 ASCVD events over the course of 107 450 person years in epoch 2, and 775 ASCVD events over the course of 119 069 person-years in epoch 3. The mean age at onset of first ASCVD event was greater in the third epoch by 8.1 years (men) to 10.3 years (women) compared with the first epoch. The RLR of ASCVD from 45 years of age declined from 43.7% in epoch 1 to 28.1% in epoch 3 (P<0.0001), a finding that was consistent in both sexes (RLR [epoch 1 versus epoch 3], 36.3% versus 26.5% [women]; 52.5% versus 30.1% [men]; P<0.001 for both). The lower RLR of ASCVD in the last 2 epochs was observed consistently across body mass index, blood pressure, cholesterol, diabetes, smoking, and Framingham risk score strata (P<0.001 for all). The RLR of coronary heart disease events and stroke declined in both sexes (P<0.001).

Conclusions: Over the past 6 decades, mean life expectancy increased and the RLR of ASCVD decreased in the community based, predominantly White Framingham study. The residual burden of ASCVD underscores the importance of continued and effective primary prevention efforts with better screening for risk factors and their effective treatment.

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Updates to COVID-19 vaccine tracker

Reporter: Aviva Lev-Ari, PhD, RN

 

On LPBI Group’s 

CORONAVIRUS, SARS-CoV-2 PORTAL @LPBI

http://lnkd.in/ePwTDxm

Launched on 3/14/2020

We cover the following Eight Pages of LPBI Group’s Coronavirus PORTAL

  1. Breakthrough News Corner
  2. Development of Medical Counter-measures for 2019-nCoV, CoVid19, Coronavirus
  3. An Epidemiological Approach
  4. Community Impact
  5. Economic Impact of The Coronavirus Pandemic
  6. Voices of Global Citizens: Impact of The Coronavirus Pandemic
  7. Diagnosis of Coronavirus Infection by Medical Imaging and Cardiovascular Impacts of Viral Infection
  8. Key Opinion Leaders Followed by LPBI

https://pharmaceuticalintelligence.com/coronavirus-portal/

Lead Curators are:

UPDATED on 3/31/2020

COVID-19 Treatment and Vaccine Tracker This document contains an aggregation of publicly available information from validated sources. It is not an endorsement of one approach or treatment over another but simply a list of all treatments and vaccines currently in development.

  • Number
  • Type of Product – Treatment
  • FDA-Approved Indications (Treatments)
  • Clinical Trials
  • Ongoing for Other Diseases
  • Developer/ Researcher
  • Current Stage of Development
  • Funding Sources
  • Anticipated Timing
  • Sources

LEGEND

  1. CCHF= Crimean-Congo Haemorrhagic Fever
  2. CHIKV = Chikungunya Virus
  3. DengV = Dengue Virus
  4. FMD = Foot and Mouth Disease
  5. EBOV = Ebola Virus
  6. HAV = Hepatitis A Virus
  7. HBV = Hepatitis B Virus
  8. HIV = Human Immunodeficiency Virus
  9. HPV = Human Papilloma Virus
  10. Inf = Influenza
  11. LASV = Lassa Fever Virus
  12. MARV = Marburg Virus
  13. MenB = Mengingitis B
  14. MERS = Middle East Respiratory Syndrome
  15. NIPV = Nipah Virus
  16. NORV = Norovirus
  17. RABV = Rabies Virus
  18. RSV = Respiratory Syncytial Virus
  19. RVF = Rift Valley Fever
  20. SARS = Severe Acute Respiratory Syndrome

  21. SIV = Simian Immunodeficiency Virus
  22. TB = Tuberculosis
  23. VEE = Venezuelan Equine Encephalitis Virus
  24. VZV = Varicella Vaccine (Chickenpox)
  25. YFV = Yellow Fever Virus
  26. ZIKV = Zika Virus L

COVID-19 Treatment and Vaccine Tracker This document contains an aggregation of publicly-available information from validated sources. It is not an endorsement of one approach or treatment over another, but simply a list of all treatments and vaccines currently in development

  • Antibodies from recovered COVID-19 patients N/A Celltrion Pre-clinical Start Phase 1 ~ Sept 2020 Korea Herald 4

  • Antibodies from recovered COVID-19 patients N/A Kamada Pre-clinical BioSpace AbbVie 5

  • Antibodies from recovered COVID-19 patients N/A Vir Biotech/WuXi Biologics/Biogen Pre-clinical Stat News Vir Biotech 6

  • Antibodies from recovered COVID-19 patients N/A Lilly/Ab-Cellera (NIH Vaccines Research Center) Pre-clinical Start Phase 1 in late July 2020 Endpoints News

SOURCE

https://milkeninstitute.org/sites/default/files/2020-03/Covid19%20Tracker%20032020v3-posting.pdf

UPDATES to COVID-19 vaccine tracker

Posted 28 January 2022 | By Jeff Craven

SOURCE

https://www.raps.org/news-and-articles/news-articles/2020/3/covid-19-vaccine-tracker

COVID-19 vaccine tracker

 

The worldwide endeavor to create a safe and effective COVID-19 vaccine is bearing fruit. Dozens of vaccines now have been authorized or approved around the globe; many more remain in development.
 
To clarify the landscape for our readers, our vaccine tracker has been split in two. The first chart details vaccine candidates that are still in development to address the lack of vaccines and access in many countries around the world; the second chart lists vaccines that are authorized or approved by one or more country. To reveal in-depth information about each candidate, select the “Details” button above the chart or click on the green plus button next to each entry.
 
Information about the unprecedented public/private partnerships spawned by the COVID-19 public health emergency now can be found below the charts.
 
Our charts are updated every other week. If you wish to submit an update or notice an issue with this data, please email Focus at news@raps.org

Updated 28 January with new information on vaccines from Pfizer/BioNTech, Moderna, AstraZeneca, Gamaleya Research Institute, Janssen Vaccines, Sinovac, Bharat Biotech/Ocugen, Anhui Zhifei Longcom Biopharmaceutical, and Novavax as well as vaccine candidates from Walvax, Valneva, GSK/Sanofi, and Senai Cimatec.




 

Vaccine candidates in development

 

SHOW/HIDE DETAILS
 

Authorized/approved vaccines

 

SHOW/HIDE DETAILS
 

 

COVID-19 vaccine initiatives

OWS: Operation Warp Speed is a collaboration of several US government departments including Health and Human Services (HHS) and subagencies, Defense, Agriculture, Energy and Veterans Affairs and the private sector. OWS has funded JNJ-78436735 (Janssen), mRNA-1273 (Moderna), and NVX‑CoV2373 (Novavax), V590 (Merck/IAVI), V591 (Merck/Themis), AZD1222 (AstraZeneca/University of Oxford), and the candidate developed by Sanofi and GlaxoSmithKline.
 
OWS is “part of a broader strategy to accelerate the development, manufacturing, and distribution of COVID-19 vaccines, therapeutics, and diagnostics.” Leaders of OWS say they could vaccinate as many as 20 million people by the end of the year and 100 million people by February.  
 
ACTIV: Within OWS, the US National Institutes of Health (NIH) has partnered with more than 18 biopharmaceutical companies in an initiative called ACTIV. ACTIV aims to fast-track development of drug and vaccine candidates for COVID-19.
 
COVPN: The COVID-19 Prevention Trials Network (COVPN) combines clinical trial networks funded by the National Institute of Allergy and Infectious Diseases (NIAID): the HIV Vaccine Trials Network (HVTN), HIV Prevention Trials Network (HPTN), Infectious Diseases Clinical Research Consortium (IDCRC), and the AIDS Clinical Trials Group.
 
COVAX: The COVAX initiative, part of the World Health Organization’s (WHO) Access to COVID-19 Tools (ACT) Accelerator, is being spearheaded by the Coalition for Epidemic Preparedness Innovations (CEPI); Gavi, the Vaccine Alliance; and WHO. The goal is to work with vaccine manufacturers to offer low-cost COVID-19 vaccines to countries. CEPI’s candidates from companies Inovio, Moderna, CureVac, Institut Pasteur/Merck/Themis, AstraZeneca/University of Oxford, Novavax, University of Hong Kong, Clover Biopharmaceuticals, and University of Queensland/CSL are part of the COVAX initiative. The US joined COVAX on 21 January. The most up-to-date forecast of COVAX’s vaccine supply can be found here. An interim distribution forecast, most recently published 3 February, can be found here.
 

 

© 2022 Regulatory Affairs Professionals Society.

SOURCE

https://www.raps.org/news-and-articles/news-articles/2020/3/covid-19-vaccine-tracker

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UK Biobank Makes Available 200,000 whole genomes Open Access

Reporter: Stephen J. Williams, Ph.D.

The following is a summary of an article by Jocelyn Kaiser, published in the November 26, 2021 issue of the journal Science.

To see the full article please go to https://www.science.org/content/article/200-000-whole-genomes-made-available-biomedical-studies-uk-effort

The UK Biobank (UKBB) this week unveiled to scientists the entire genomes of 200,000 people who are part of a long-term British health study.

The trove of genomes, each linked to anonymized medical information, will allow biomedical scientists to scour the full 3 billion base pairs of human DNA for insights into the interplay of genes and health that could not be gleaned from partial sequences or scans of genome markers. “It is thrilling to see the release of this long-awaited resource,” says Stephen Glatt, a psychiatric geneticist at the State University of New York Upstate Medical University.

Other biobanks have also begun to compile vast numbers of whole genomes, 100,000 or more in some cases (see table, below). But UKBB stands out because it offers easy access to the genomic information, according to some of the more than 20,000 researchers in 90 countries who have signed up to use the data. “In terms of availability and data quality, [UKBB] surpasses all others,” says physician and statistician Omar Yaxmehen Bello-Chavolla of the National Institute for Geriatrics in Mexico City.

Enabling your vision to improve public health

Data drives discovery. We have curated a uniquely powerful biomedical database that can be accessed globally for public health research. Explore data from half a million UK Biobank participants to enable new discoveries to improve public health.

Data Showcase

Future data releases

This UKBB biobank represents genomes collected from 500,000 middle-age and elderly participants for 2006 to 2010. The genomes are mostly of a European descent. Other large scale genome sequencing ventures like Iceland’s DECODE, which collected over 100,000 genomes, is now a subsidiary of Amgen, and mostly behind IP protection, not Open Access as this database represents.

UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database is regularly augmented with additional data and is globally accessible to approved researchers undertaking vital research into the most common and life-threatening diseases. It is a major contributor to the advancement of modern medicine and treatment and has enabled several scientific discoveries that improve human health.

A summary of some large scale genome sequencing projects are show in the table below:

BiobankCompleted Whole GenomesRelease Information
UK Biobank200,000300,000 more in early 2023
TransOmics for
Precision Medicien
161,000NIH requires project
specific request
Million Veterans
Program
125,000Non-Veterans Affairs
researchers get first access
100,000 Genomes
Project
120,000Researchers must join Genomics
England collaboration
All of Us90,000NIH expects to release 2022

Other Related Articles on Genome Biobank Projects in this Open Access Online Scientific Journal Include the Following:

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

Exome Aggregation Consortium (ExAC), generated the largest catalogue so far of variation in human protein-coding regions: Sequence data of 60,000 people, NOW is a publicly accessible database

Systems Biology Analysis of Transcription Networks, Artificial Intelligence, and High-End Computing Coming to Fruition in Personalized Oncology

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

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Defective viral RNA sensing gene OAS1 linked to severe COVID-19

Reporter: Stephen J. Williams, Ph.D.

Source: https://www.science.org/doi/10.1126/science.abm3921

Defective viral RNA sensing linked to severe COVID-19

JOHN SCHOGGINS SCIENCE•28 Oct 2021•Vol 374, Issue 6567•pp. 535-536•DOI: 10.1126/science.abm39214,824

Why do some people with COVID-19 get sicker than others? Maybe exposure to a particularly high dose of the causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), accounts for the difference. Perhaps deficiencies in diet, exercise, or sleep contribute to worse illness. Although many factors govern how sick people become, a key driver of the severity of COVID-19 appears to be genetic, which is common for other human viruses and infectious agents (1). On page 579 of this issue, Wickenhagen et al. (2) show that susceptibility to severe COVID-19 is associated with a single-nucleotide polymorphism (SNP) in the human gene 2′-5′-oligoadenylate synthetase 1 (OAS1).The authors reasoned that SARS-CoV-2 should be inhibited by interferon-mediated antiviral responses, which are among the first cellular defense mechanisms produced in response to a viral infection. Interferons are a group of cytokines that induce the transcription of a large cadre of genes, many of which encode proteins with the potential to directly inhibit the invading virus. Wickenhagen et al. interrogated many hundreds of these putative antiviral proteins for their ability to suppress SARS-CoV-2 in cultured cells and found that OAS1 was particularly potent against SARS-CoV-2.OAS1 is an enzyme that is activated in the presence of double-stranded RNA, which is scattered along an otherwise singlestranded SARS-CoV-2 genome because of an assortment of RNA hairpins and other secondary structures. Once activated, OAS1 catalyzes the polymerization of adenosine triphosphate (ATP) into a second messenger, 2′-5′-oligoadenylate. This then triggers the conversion of ribonuclease L (RNaseL) into its active form so that it can cleave viral RNA, effectively blunting viral replication (3). Wickenhagen et al. found that OAS1 is expressed in respiratory tissues of healthy donors and COVID-19 patients and that it interacts with a region of the SARS-CoV-2 genome that contains double-stranded RNA secondary structures (see the figure).OAS1 exists predominantly as two isoforms in humans—a longer isoform (p46) and a shorter version (p42). Genetic variation dictates which isoform will be expressed. In humans, p46 is expressed in people who have a SNP that causes alternative splicing of the OAS1 messenger RNA (mRNA). This results in the utilization of a terminal exon that is not used to translate p42. Thus, the carboxyl terminus of the p46 OAS1 protein contains a distinct four–amino acid motif that forms a prenylation site. Prenylation is a posttranslational modification that targets proteins to membranes. In cell culture experiments, Wickenhagen et al. showed that only OAS1 p46, but not p42, could inhibit SARS-CoV-2. However, when the prenylation site of p46 was engineered into p42, this chimeric p42 protein was able to inhibit SARS-CoV-2, which strongly implicates a role for OAS1 specifically at membranes.Why are membranes important? SARS-CoV-2, like all coronaviruses, co-opts cellular membranes at the endoplasmic reticulum to form double-membrane vesicles, in which the virus replicates its genome. Thus, membrane-bound OAS1 p46 may be specifically activated by RNA viruses that form membrane-bound vesicles for replication. Indeed, the unrelated cardiovirus A, which also forms vesicular membranous structures, was inhibited by OAS1. Conversely, other respiratory RNA viruses, such as human parainfluenza virus type 3 and human respiratory syncytial virus, which do not use membrane-tethered vesicles for replication, were not inhibited by p46.Wickenhagen et al. examined a cohort of 499 COVID-19 patients hospitalized in the UK. Whereas all patients expressed OAS1, 42.5% of them did not express the antiviral p46 isoform. These patients were statistically more likely to have severe COVID-19 (be admitted to the intensive care unit). This suggests that OAS1 is an important antiviral factor in the control of SARS-CoV-2 infection and that its inability to activate RNaseL results in prolonged infections and severe disease, although other factors likely contribute. The authors also examined animals known to harbor different coronaviruses. They found evidence for prenylated OAS1 proteins in mice, cows, and camels. Notably, horseshoe bats, which are considered a possible reservoir for SARS-related coronaviruses (4), lack a prenylation motif in their OAS1 because of genomic changes that eliminated the critical four-amino acid motif. A horseshoe bat (Rhinolophus ferrumequinum) OAS1 was unable to inhibit SARS-CoV-2 infection in cell culture. Conversely, the black flying fox (Pteropus alecto)—a pteropid bat that is a reservoir for the Nipah and Hendra viruses, which can also infect humans—possesses a prenylated OAS1 that can inhibit SARS-CoV-2. These findings indicate that horseshoe bats may be genetically and evolutionarily primed to be optimal reservoir hosts for certain coronaviruses, like SARS-CoV-2.Other studies have now shown that the p46 OAS1 variant, which resides in a genomic locus inherited from Neanderthals (57), correlates with protection from COVID-19 severity in various populations (89). These findings mirror previous studies indicating that outcomes with West Nile virus (10) and hepatitis C virus (11) infection, both of which also use membrane vesicles for replication, are also associated with genetic variation at the human OAS1 locus. Another elegant functional study complements the findings of Wickenhagen et al. by also demonstrating that prenylated OAS1 inhibits multiple viruses, including SARS-CoV-2, and is associated with protection from severe COVID-19 in patients (12).There is a growing body of evidence that provides critical understanding of how human genetic variation shapes the outcome of infectious diseases like COVID-19. In addition to OAS1, genetic variation in another viral RNA sensor, Toll-like receptor 7 (TLR7), is associated with severe COVID-19 (1315). The effects appear to be exclusive to males, because TLR7 is on the X chromosome, so inherited deleterious mutations in TLR7 therefore result in immune cells that fail to produce normal amounts of interferon, which correlates with more severe COVID-19. Our knowledge of the host cellular factors that control SARS-CoV-2 is rapidly increasing. These findings will undoubtedly open new avenues into SARS-CoV-2 antiviral immunity and may also be beneficial for the development of strategies to treat or prevent severe COVID-19.

References and Notes

1J. L. Casanova, Proc. Natl. Acad. Sci. U.S.A.112, E7118 (2015).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR2A. Wickenhagen et al., Science374, eabj3624 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR3H. Kristiansen, H. H. Gad, S. Eskildsen-Larsen, P. Despres, R. Hartmann, J. Interferon Cytokine Res.31, 41 (2011).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR4S. Lytras, W. Xia, J. Hughes, X. Jiang, D. L. Robertson, Science373, 968 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR5S. Zhou et al., Nat. Med.27, 659 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR6H. Zeberg, S. Pääbo, Proc. Natl. Acad. Sci. U.S.A.118, e2026309118 (2021).CROSSREFPUBMEDGOOGLE SCHOLAR7F. L. Mendez, J. C. Watkins, M. F. Hammer, Mol. Biol. Evol.30, 798 (2013).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR8A. R. Banday et al., medRxiv2021).GO TO REFERENCECROSSREFGOOGLE SCHOLAR9E. Pairo-Castineira et al., Nature591, 92 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR10J. K. Lim et al., PLOS Pathog.5, e1000321 (2009).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR11M. K. El Awady et al., J. Gastroenterol. Hepatol.26, 843 (2011).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR12F. W. Soveg et al., eLife10, e71047 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR13T. Asano et al., Sci. Immunol.6, eabl4348 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR14C. Fallerini et al., eLife10, e67569 (2021).CROSSREFPUBMEDGOOGLE SCHOLAR15C. I. van der Made et al., JAMA324, 663 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR

For more on COVID-19 Please see our Coronavirus Portal at

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Moderna Vaccine Patent Application needs to include Names of Three NIH Scientists that Shared the Genome Sequence of SAR-Cov-2 with Moderna Early on

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 11/12/2021

Within the filing, Moderna said it had “reached the good-faith determination” that three NIH scientists — John Mascola, Barney Graham and Kizzmekia Corbett — “did not co-invent” the sequence that prompts the body’s immune response to the coronavirus spike protein. The NIH, meanwhile, says the trio worked with Moderna at the outset of the pandemic to design the component in question.

In response to an Endpoints News request for comment, a Moderna spokesperson said the company has “all along” recognized the role the NIH played in developing the Covid-19 shot. But the spokesperson insisted only Moderna scientists invented mRNA-1273 — the codename for the company’s vaccine.

In the new book A Shot to Save the World out last month detailing the inventions of the mRNA Covid-19 vaccines, Wall Street Journal reporter Gregory Zuckerman wrote the three NIH scientists in question designed a sequence for a vaccine and sent it to Moderna. The biotech then used it to confirm their own designs and produce that vaccine.

Zuckerman wrote:

On Thursday, January 23, Wang packed his material in a container, trying hard to ensure it didn’t leak, and shipped it all to Kizzmekia Corbett, the government scientist who was doing similar work with other’s in Graham’s lab. Corbett, Graham and John Mascola chose an ideal spike-protein design and sent it to Moderna. The company’s scientists, relying on McLellan and Wang’s earlier work, had built their own spike-protein design. It matched the one from the government scientists, confirming they made the right choice. Moderna took their chosen sequence, employed some sophisticated computer software, and built an mRNA molecule capable of producing the stabilized spike protein. This would become Moderna’s vaccine antigen.

SOURCE

What Moderna says: The company argues that the NIH scientists — John Mascola, Barney Graham and Kizzmekia Corbett — were not part of selecting the messenger RNA sequence that became the Covid-19 shot authorized today. That sequence patent is essentially the heart of the product.

Moderna “has recognized the substantial role that the NIAID has played” in the vaccine development by including those scientists on other patents but “just because someone is an inventor on one patent application relating to our COVID-19 vaccine does not mean they are an inventor on every patent application relating to the vaccine,” it tweeted.

“Moderna remains the only company to have pledged not to enforce its COVID-19 intellectual property during the pandemic,” the company added.

It’s far from over: Moderna, which never brought a product to market before its effective Covid-19 shot, has received nearly $10 billion in government funding for the vaccine — a figure that advocates return to repeatedly when pressing for global access to patents and production.

SOURCE

From: POLITICO Pulse <pulse@email.politico.com>
Reply-To: “POLITICO, LLC” <reply-fe8c1d737662017574-630320_HTML-638333449-1376319-0@politicoemail.com>
Date: Friday, November 12, 2021 at 10:02 AM
To: Aviva Lev-Ari <Avivalev-ari@alum.Berkeley.edu>
Subject: Moderna vs. The Government

11/9/2021 and 11/11/2021

The NIH told the New York Times earlier this week that three of its scientists — John Mascola, Barney Graham, who recently retired, and Kizzmekia Corbett, who has since moved over to Harvard — worked with Moderna to design the genetic sequence that prompts the vaccine to produce an immune response.

“I think Moderna has made a serious mistake here in not providing the kind of co-inventorship credit to the people who played a major role in the development of the vaccine that they are now making a fair amount of money on. We did our best to try to resolve this and ultimately failed but we are not done,” NIH Director Francis Collins told Reuters in an interview yesterday.

Dr. Barney Graham, left, and his colleague at the time, Dr. Kizzmekia Corbett, right, explaining the role of spike proteins to President Biden at the National Institutes of Health in Bethesda, Md., in February 2021

The vaccine grew out of a four-year collaboration between Moderna and the N.I.H., the government’s biomedical research agency — a partnership that was widely hailed when the shot was found to be highly effective. A year ago this month, the government called it the “N.I.H.-Moderna Covid-19 vaccine.”

The agency says three scientists at its Vaccine Research Center — Dr. John R. Mascola, the center’s director; Dr. Barney S. Graham, who recently retired; and Dr. Kizzmekia S. Corbett, who is now at Harvard — worked with Moderna scientists to design the genetic sequence that prompts the vaccine to produce an immune response, and should be named on the “principal patent application.”

https://www.nytimes.com/2021/11/09/us/moderna-vaccine-patent.html?referringSource=articleShare

If the three agency scientists are named on the patent along with the Moderna employees, the federal government could have more of a say in which companies manufacture the vaccine, which in turn could influence which countries get access. It would also secure a nearly unfettered right to license the technology, which could bring millions into the federal treasury.

“Omitting N.I.H. inventors from the principal patent application deprives N.I.H. of a co-ownership interest in that application and the patent that will eventually issue from it.”

According to the NYT article,

But experts said the disputed patent was the most important one in Moderna’s growing intellectual property portfolio. It seeks to patent the genetic sequence that instructs the body’s cells to make a harmless version of the spike proteins that stud the surface of the coronavirus, which triggers an immune response.

While it has not publicly acknowledged the rift until now, the Biden administration has expressed frustration that Moderna has not done more to provide its vaccine to poorer nations even as it racks up huge profits.

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Epidemiological measurement on COVID-19 pandemic may have statistical biases which might affect next variant responses

Reporter: Stephen J. Williams Ph.D.

Source: https://www.science.org/doi/10.1126/science.abi6602

From the jounal Science

Tackling the pandemic with (biased) data

CHRISTINA PAGEL AND CHRISTIAN A. YATESSCIENCE•22 Oct 2021•Vol 374, Issue 6566•pp. 403-404•DOI: 10.1126/science.abi66027,757

Accurate and near real-time data about the trajectory of the COVID-19 pandemic have been crucial in informing mitigation policies. Because choosing the right mitigation policies relies on an accurate assessment of the current state of the local epidemic, the potential ramifications of misinterpreting data are serious. Each data source has inherent biases and pitfalls in interpretation. The more data sources that are interpreted in combination, the easier it is to detect genuine changes in an epidemic. Recently, in many countries, this has involved disentangling the varying impact of rising but heterogeneous vaccination rates, relaxation of mitigations, and the emergence of new variants such as Delta.The exact data collected and their accuracy will vary by country. Typical data common to many countries are numbers of tests, confirmed cases, hospital and intensive care unit (ICU) admissions and occupancy, deaths, and vaccinations (1). Many countries additionally sequence a proportion of new positive tests to identify and track emerging variants. Some countries also now collect and publish data on infections, hospitalizations, and deaths by vaccination status (e.g., Israel and the UK). Stratifying all available data by different demographic factors (e.g., age, location, measures of deprivation, and ethnicity) is crucial for understanding patterns of spread, potential impact of policies, and efficacy of vaccines (age, timing of breakthrough infections, and prevalent variants).It is also necessary to be aware of what data are not being collected. For example, persistent symptoms of COVID-19 (Long Covid) were recognized as a long-term adverse outcome by the autumn of 2020. However, no simple diagnostic test has been associated with the up to 200 different reported symptoms (2). Counting Long Covid relies on a clinical diagnosis, based on a history of having had COVID-19 and a failure to fully recover, with development of some characteristic symptoms and with no obvious alternative cause (3). These features make it very difficult to measure routinely, and so it rarely is. As a result, Long Covid is often neglected in decision-making. Failure to account for the disease load associated with Long Covid may lead to an unnecessary long-term societal health burden.The feedback between different types of outcomes, different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, different mitigation policies (including vaccination), and individual risks (a combination of exposure and clinical risk) is complex and must be factored into both interpretation of data and the development of policy. Using all available data to quantify transmission is crucial to ensuring rapid and effective responses to early phases of renewed exponential growth and to evaluating mitigation measures. Relying too much on a single data source, or without disaggregating data, risks fundamentally misunderstanding the state of the epidemic.The inherent biases and lags in data are particularly important to understand from the point of view of policy-makers. Because of the natural time scales of COVID-19 disease progression (see the figure), policy changes can take several weeks to show up in the data. Purely reactive policy-making is likely to be ineffective. When cases are rising, increases in hospital admissions and deaths will follow. When a new variant is outcompeting existing strains, it is likely to become dominant without action to suppress. The precautionary principle suggests acting early and emphatically. Conversely, when releasing restrictions, governments must wait long enough to assess them before continuing with re-opening.The most up-to-date indicator of the state of the epidemic is typically the number of confirmed cases, as ascertained through testing of both symptomatic individuals and those tested frequently regardless of symptoms. Symptom-based testing is likely to pick up more adults and fewer younger individuals (4). Infections in children are harder to detect: children are more likely to be asymptomatic than adults, are harder to administer tests to (particularly young children), are often exposed to other viruses with similar symptoms, and can present with symptoms that are atypical in adults (e.g., abdominal pain or nausea). Children under 12 are not routinely offered the COVID-19 vaccination, and their mixing in schools provides ongoing opportunities for the virus to circulate, so it will be important for countries to track infections in children as accurately as possible. Other testing biases include accessibility, reporting lags, and the ability to act lawfully upon receiving a positive result. Substantial changes in the number of people seeking tests may further confound case figures (5). Case positivity rates may provide a more accurate reflection of the state of the epidemic (6) but are dependent on the mix of symptomatic and asymptomatic people being tested.SARS-CoV-2 variants have been an important driver of local epidemics in 2021. The four main SARS-CoV-2 variants of concern, to date, are B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), and B.1.617.2 (Delta). Some have been more transmissible (Alpha), some have substantial resistance to previous infection or vaccines (Beta), and some have elements of both (Gamma and Delta) (7). Currently, the high transmissibility of Delta combined with some immune evasion has made it the world’s dominant variant. Determining which variants pose a substantial threat is difficult and takes time, particularly when many variants cocirculate. This is especially true for situations in which a dominant variant is declining, and a new one growing. While the declining variant remains dominant, its decrease masks increases in the new variant because case numbers remain unchanged or fall overall. Only when a new variant becomes dominant does its growth become apparent in aggregated case data, by which time it is, by definition, too late to contain its spread. This dynamic has been observed across the world with Delta over the latter half of 2021.With multiple variants circulating, there are, effectively, multiple epidemics occurring in parallel, and they must be tracked separately. This typically requires the availability of sequencing data, which is unfortunately limited in most countries. Sequencing takes time and so is typically a few weeks out of date. These lags, and the uncertainty in sampling, can lead to hesitancy in acting. The rapid path to dominance of the Delta variant in the UK highlights the need for action when a quickly growing variant represents a few percent (or less) of overall cases.Hospital admissions or occupancy data do not suffer the same biases associated with testing behaviors and provide unequivocal evidence of widespread transmission, its geography, and demographics. However, hospital admissions lag infections more than reported cases do, rendering these data less useful for proactive decision-making. Hospital data are also biased toward older people, who are more likely to suffer severe COVID-19, and now, unvaccinated populations. ICU occupancy data show a younger age profile than admissions because younger patients have a better chance of benefitting from the invasive treatment procedures (8).Deaths are the most lagged indicator, typically occurring 3 or more weeks after infection and with an additional lag in registration and reporting. Death data should never be used to inform real-time policy decisions. Instead, death figures can act as an eventual measure of the success of a country’s epidemic strategy and implementation. The age distribution of those who eventually die from COVID-19 is different from other metrics of the epidemic—skewed furthest toward older age groups (9). Those with clinical risk factors (such as immunodeficiency, obesity, or existing lung conditions), high exposure (health care workers and low-income workers), and the unvaccinated are overrepresented in COVID-19 deaths.In countries with high vaccination rates, vaccination has had a substantial impact—reducing COVID-19 cases, hospitalizations, and deaths. However, when looking at the raw numbers in highly vaccinated populations, it can be the case that more fully vaccinated people are dying of COVID-19 than unvaccinated. If these raw statistics are misinterpreted—or worse, deliberately misused—they can damage vaccine confidence. More vaccinated people may die than unvaccinated because such a high proportion of people are vaccinated (10). This does not mean vaccines are not effective at preventing death. Looking at the rates of death in vaccinated and unvaccinated individuals separately within age groups demonstrates that vaccines provide considerable protection against severe disease and death. This example illustrates how important it is to curate and manage the way in which data are presented.

COVID-19 progressionAn approximate timeline from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to various outcomes. When current infections show up in different data sources depends on this timeline. Collecting data for Long Covid, asymptomatic infection, and vaccine history will improve understanding of the pandemic.GRAPHIC: N. CARY/SCIENCE

Each country has established its own vaccination priority lists and dosing schedules to best achieve its goals (1112). Each of these strategies will manifest differently in the data. Additionally, many countries are using multiple vaccines in tandem and administer them differently for different demographics. Some countries are vaccinating adolescents, and others are not or not offering them the full approved dose. Most vaccines require two doses, spaced between 3 and 12 weeks apart, except for the Johnson & Johnson single-dose vaccine. This matters, particularly as variants spread, because different vaccines have different effectiveness after one and two doses, different timelines to full effectiveness, and different effectiveness against variants (13).Data published on the vaccination delivery itself must thus go beyond the raw numbers of people vaccinated. Vaccine uptake must be reported by whether fully or partially (one-dose in a two-dose regimen) vaccinated and using the whole population as a denominator. It is vital to disaggregate vaccine data by age, gender, and ethnicity as well as location so that it is possible, for example, to understand the impact of deprivation on vaccine coverage or vaccine hesitancy in particular demographics. When interpreting vaccination data, it is important to remember that there is also a lag between delivery and the build-up of immunity.Data on reinfection and post-vaccination (breakthrough) infection are also important to determine the relative benefits of infection-mediated and vaccine-mediated immunity and the length of protection offered. Studies that show those who were immunized earlier were acquiring COVID-19 with higher rates than those vaccinated more recently may suggest waning vaccine protection (14). Such studies have already prompted vaccine booster programs in some countries. However, any study that suggests waning immunity must be extremely careful to ensure that the “early” and “recent” subgroups are properly controlled. Differences in prior exposure, affluence, education level, age, and other demographic factors between these cohorts may be enough to explain the disparities in SARS-CoV-2 infection rates, even in the absence of waning immunity. Waning immunity must also be reported separately for different outcomes; for example, there might be waning in terms of preventing symptomatic infection but far less or none in preventing death (15). Additionally, there are ethical concerns about mass booster programs in high-income countries while many lower-income countries have been unable to procure vaccines.Moving into the vaccination era, reported cases, hospitalizations, and deaths should also be disaggregated by vaccination status (and by which vaccine), which will be easier in countries where national linked datasets exist. Additionally, incorporating Long Covid into routine reporting and policy-making is crucial. Consistent diagnostic criteria and well-controlled studies will be vital to this effort. These elusive data will be of critical importance to navigate our way successfully out of the pandemic.

Acknowledgments

C.P. and C.A.Y. are both members of Independent SAGE: www.independentsage.org.

References and Notes

1M. Roser et al., Our World in Data (2021); https://bit.ly/3kepLgw.GO TO REFERENCEGOOGLE SCHOLAR2H. E. Davis et al., E. Clin. Med.38, 101019 (2021).GO TO REFERENCEGOOGLE SCHOLAR3M. Sivan, S. Taylor, BMJ371, m4938 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR4S. M. Moghadas et al., Proc. Natl. Acad. Sci. U.S.A.117, 17513 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR5J. Wise, BMJ370, m3678 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR6D. Dowdy, G. D’Souza, COVID-19 Testing: Understanding the “Percent Positive” (2020); https://bit.ly/3CeN8wl.GO TO REFERENCEGOOGLE SCHOLAR7C. E. Gómez et al., Vaccines (Basel)9, 243 (2021).CROSSREFPUBMEDGOOGLE SCHOLAR8A. B. Docherty et al., BMJ369, 1985 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR9Office for National Statistics, Deaths registered weekly in England and Wales by age and sex: covid-19 (2021); https://bit.ly/3Ci2obS.

For articles on Issues of Bias in Science on this Open Access Journal see

From @Harvardmed Center for Bioethics: The Medical Ethics of the Corona Virus Crisis

Live Notes from @HarvardMed Bioethics: Authors Jerome Groopman, MD & Pamela Hartzband, MD, discuss Your Medical Mind

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Comparative Study: Four SARS-CoV-2 vaccines induce quantitatively different antibody responses against SARS-CoV-2 variants

Reporter: Aviva Lev- Ari, PhD, RN

Marit J. van Gils, A. H. Ayesha Lavell, Karlijn van der Straten, Brent Appelman, Ilja Bontjer, Meliawati Poniman, Judith A. Burger, Melissa Oomen, Joey H. Bouhuijs, Lonneke A. van Vught, Marleen A. Slim, Michiel Schinkel, Elke Wynberg, Hugo D.G. van Willigen, Marloes Grobben, Khadija Tejjani, Jonne Snitselaar, Tom G. Caniels, Amsterdam UMC COVID-19 S3/HCW study group, Alexander P. J. Vlaar, Maria Prins, Menno D. de Jong, Godelieve J. de Bree, Jonne J. Sikkens, Marije K. Bomers, Rogier W. Sanders doi: https://doi.org/10.1101/2021.09.27.21264163

Abstract

Emerging and future SARS-CoV-2 variants may jeopardize the effectiveness of vaccination campaigns. We performed a head-to-head comparison of the ability of sera from individuals vaccinated with either one of four vaccines (BNT162b2, mRNA-1273, AZD1222 or Ad26.COV2.S) to recognize and neutralize the four SARS-CoV-2 variants of concern (VOCs; Alpha, Beta, Gamma and Delta). Four weeks after completing the vaccination series, SARS-CoV-2 wild-type neutralizing antibody titers were highest in recipients of BNT162b2 and mRNA-1273 (median titers of 1891 and 3061, respectively), and substantially lower in those vaccinated with the adenovirus vector-based vaccines AZD1222 and Ad26.COV2.S (median titers of 241 and 119, respectively). VOCs neutralization was reduced in all vaccine groups, with the largest (5.8-fold) reduction in neutralization being observed against the Beta variant. Overall, the mRNA vaccines appear superior to adenovirus vector-based vaccines in inducing neutralizing antibodies against VOCs four weeks after the final vaccination.

Figure 2:Binding and neutralization titers post-vaccination against VOCs.

(A) Median with interquartile range of binding titers to wild-type and VOCs S proteins represented as mean fluorescence intensity (MFI) of 1:100,000 diluted sera collected four-five weeks after full vaccination for the four vaccination groups. The lower cutoff for binding was set at an MFI of 10 (grey shading). Vaccine groups are indicated by colors with BNT162b2 in green, mRNA-1273 in purple, AZD1222 in orange and Ad26.COV2.S in blue. (B) Median with interquartile range of half-maximal neutralization (ID50) titers of D614G and VOCs pseudoviruses for sera collected after full vaccination for the four vaccination groups. The lower cutoff for neutralization was set at an ID50 of 100 (grey shading). Vaccine groups are indicated by colors with BNT162b2 in green, mRNA-1273 in purple, AZD1222 in orange and Ad26.COV2.S in blue. (C) Median ID50 neutralization of D614G and VOCs plotted against the reported vaccine efficacy against symptomatic infection25,1217. Vaccine groups are indicated by colors with BNT162b2 in green, mRNA-1273 in purple, AZD1222 in orange and Ad26.COV2.S in blue. Circles represent WT data, squares for Alpha, diamond for Beta, nabla triangle for Gamma and delta triangle for Delta. Spearman’s rank correlation coefficient with p value are indicated. The result of the AZD1222 phase 3 trial conducted in South Africa, demonstrating poor (10%) efficacy against Beta variant, is not shown.

SOURCE

 https://doi.org/10.1101/2021.09.27.21264163

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